AI SearchEmpirical and modeling approach for flash flood warning system at MaeSai, Thailand
Call Number: AIT Thesis no.WM-24-16 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. WM-24-16Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2024Description: 199 leaves : ill.+ 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M. Sc.) - Asian Institute of Technology, 2024 Summary: Mae Sai District, Chiang Rai located at border between Thailand and Myanmar, have the shared market with experiences recurrent flash flood. Main impact of the flash flood was property damage for business villagers. The present study aimed to analyze meteo hydrological conditions that cause flash floods. Then, develop empirical and modeling approach. Integrate the flood criteria, and finally propose flood warning framework. Rainfall data is limited and not available in Myanmar side. Eight rainfall stations and water level data from Thailand side were collected to analyze the rainfall, flood characteristics, and flood criteria. There are totally 13 flood events from years 2017 to 2022. Fieldwork and field investigation were carried out to have comprehensive information during the flood. The flood model is to use RRI model as a computational tool with Global Satellite Mapping of Precipitation NOW (GSMaP NOW) as satellite rainfall data. The results showed that the peak water level and 15-minute average weighted rainfall had the strongest correlation (0.73) among other meteo-hydrological parameters correlation, indicating that higher flood peak levels occur with heavier rainfall. However, flooding in the Mae Sai can occur even when rainfall is moderate because the initial water level in Sai River is high at the time. It was found that even 20 mm of rainfall within 24 hours could cause flooding at a high water level of 1.6 meters. Therefore, the flood is classified to the type of single and consecutive rainfall. Its criteria are based on rainfall and initial water level. For flood model calibration and validation, it provides 0.83 and 0.19 in terms of NSE respectively. A fieldwork and field investigation revealed that Mae Sai people need flood warnings 3-6 hours in advance, and flood warning using the public address speaker and broadcasting tower is the most required. Flood warning system framework was developed based on the present work both technical, management, and communication aspects.
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A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Water Engineering and Management
Thesis (M. Sc.) - Asian Institute of Technology, 2024
Mae Sai District, Chiang Rai located at border between Thailand and Myanmar, have the shared market with experiences recurrent flash flood. Main impact of the flash flood was property damage for business villagers. The present study aimed to analyze meteo hydrological conditions that cause flash floods. Then, develop empirical and modeling approach. Integrate the flood criteria, and finally propose flood warning framework. Rainfall data is limited and not available in Myanmar side. Eight rainfall stations and water level data from Thailand side were collected to analyze the rainfall, flood characteristics, and flood criteria. There are totally 13 flood events from years 2017 to 2022. Fieldwork and field investigation were carried out to have comprehensive information during the flood. The flood model is to use RRI model as a computational tool with Global Satellite Mapping of Precipitation NOW (GSMaP NOW) as satellite rainfall data. The results showed that the peak water level and 15-minute average weighted rainfall had the strongest correlation (0.73) among other meteo-hydrological parameters correlation, indicating that higher flood peak levels occur with heavier rainfall. However, flooding in the Mae Sai can occur even when rainfall is moderate because the initial water level in Sai River is high at the time. It was found that even 20 mm of rainfall within 24 hours could cause flooding at a high water level of 1.6 meters. Therefore, the flood is classified to the type of single and consecutive rainfall. Its criteria are based on rainfall and initial water level. For flood model calibration and validation, it provides 0.83 and 0.19 in terms of NSE respectively. A fieldwork and field investigation revealed that Mae Sai people need flood warnings 3-6 hours in advance, and flood warning using the public address speaker and broadcasting tower is the most required. Flood warning system framework was developed based on the present work both technical, management, and communication aspects.
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